Agent skill

bio-pathway-go-enrichment

Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and customizable statistical thresholds.

View SKILL.md on GitHub Repository
Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-pathway-go-enrichment

SKILL.md

Version Compatibility

Reference examples tested with: R stats (base), clusterProfiler 4.10+

Before using code patterns, verify installed versions match. If versions differ:

  • R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

GO Over-Representation Analysis

Core Pattern

Goal: Identify enriched Gene Ontology terms in a gene list from differential expression or similar analyses.

Approach: Test for over-representation of GO terms using the hypergeometric test via clusterProfiler enrichGO.

"Run GO enrichment on my gene list" → Test whether biological process, molecular function, or cellular component terms are over-represented among significant genes.

r
library(clusterProfiler)
library(org.Hs.eg.db)  # Human - change for other organisms

ego <- enrichGO(
    gene = gene_list,           # Character vector of gene IDs
    OrgDb = org.Hs.eg.db,       # Organism annotation database
    keyType = 'ENTREZID',       # ID type: ENSEMBL, SYMBOL, ENTREZID, etc.
    ont = 'BP',                 # BP, MF, CC, or ALL
    pAdjustMethod = 'BH',       # p-value adjustment method
    pvalueCutoff = 0.05,
    qvalueCutoff = 0.2
)

Prepare Gene List from DE Results

Goal: Extract significant gene IDs from differential expression results and convert to the format required by enrichGO.

Approach: Filter DE results by adjusted p-value and fold change, then convert gene symbols to Entrez IDs using bitr.

r
library(dplyr)

de_results <- read.csv('de_results.csv')

sig_genes <- de_results %>%
    filter(padj < 0.05, abs(log2FoldChange) > 1) %>%
    pull(gene_id)

# If using gene symbols, convert to Entrez IDs
gene_ids <- bitr(sig_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)
gene_list <- gene_ids$ENTREZID

ID Conversion with bitr

Goal: Convert between gene identifier types (Ensembl, Symbol, Entrez) for compatibility with enrichment tools.

Approach: Use clusterProfiler bitr to map between ID types using organism annotation databases.

r
# Check available key types
keytypes(org.Hs.eg.db)

# Convert between ID types
converted <- bitr(genes, fromType = 'ENSEMBL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)

# Multiple output types
converted <- bitr(genes, fromType = 'SYMBOL', toType = c('ENTREZID', 'ENSEMBL'), OrgDb = org.Hs.eg.db)

With Background Universe

Goal: Improve enrichment specificity by restricting the background to genes actually tested in the experiment.

Approach: Pass all expressed genes (not just significant ones) as the universe parameter to enrichGO.

r
# Use all expressed genes as background (recommended)
all_genes <- de_results$gene_id
universe_ids <- bitr(all_genes, fromType = 'SYMBOL', toType = 'ENTREZID', OrgDb = org.Hs.eg.db)

ego <- enrichGO(
    gene = gene_list,
    universe = universe_ids$ENTREZID,  # Background gene set
    OrgDb = org.Hs.eg.db,
    keyType = 'ENTREZID',
    ont = 'BP',
    pAdjustMethod = 'BH',
    pvalueCutoff = 0.05
)

All Three Ontologies

r
# Run all ontologies at once
ego_all <- enrichGO(
    gene = gene_list,
    OrgDb = org.Hs.eg.db,
    keyType = 'ENTREZID',
    ont = 'ALL',  # BP, MF, and CC combined
    pAdjustMethod = 'BH',
    pvalueCutoff = 0.05
)

# Results include ONTOLOGY column
head(as.data.frame(ego_all))

Make Results Readable

r
# Convert Entrez IDs to gene symbols in results
ego_readable <- setReadable(ego, OrgDb = org.Hs.eg.db, keyType = 'ENTREZID')

# Or use readable = TRUE directly (only works with ENTREZID input)
ego <- enrichGO(
    gene = gene_list,
    OrgDb = org.Hs.eg.db,
    keyType = 'ENTREZID',
    ont = 'BP',
    readable = TRUE  # Converts to symbols
)

Extract and Export Results

r
# View top results
head(ego)

# Convert to data frame
results_df <- as.data.frame(ego)

# Key columns: ID, Description, GeneRatio, BgRatio, pvalue, p.adjust, qvalue, geneID, Count

# Export to CSV
write.csv(results_df, 'go_enrichment_results.csv', row.names = FALSE)

# Filter for specific criteria
sig_terms <- results_df[results_df$p.adjust < 0.01 & results_df$Count >= 5, ]

Simplify Redundant Terms

Goal: Remove highly similar GO terms to reduce redundancy in enrichment results.

Approach: Cluster GO terms by semantic similarity and retain representative terms using the simplify function.

r
# Remove redundant GO terms (keeps representative terms)
ego_simplified <- simplify(ego, cutoff = 0.7, by = 'p.adjust', select_fun = min)

Different Organisms

r
# Mouse
library(org.Mm.eg.db)
ego_mouse <- enrichGO(gene = genes, OrgDb = org.Mm.eg.db, ont = 'BP')

# Zebrafish
library(org.Dr.eg.db)
ego_zfish <- enrichGO(gene = genes, OrgDb = org.Dr.eg.db, ont = 'BP')

# Yeast
library(org.Sc.sgd.db)
ego_yeast <- enrichGO(gene = genes, OrgDb = org.Sc.sgd.db, ont = 'BP', keyType = 'ORF')

Group GO Terms by Ancestor

Goal: Classify genes by broad GO slim categories for a high-level functional overview.

Approach: Use groupGO to assign genes to GO terms at a specific hierarchy level.

r
# Classify genes by GO slim categories
ggo <- groupGO(
    gene = gene_list,
    OrgDb = org.Hs.eg.db,
    ont = 'BP',
    level = 3,  # GO hierarchy level
    readable = TRUE
)

Key Parameters

Parameter Default Description
gene required Vector of gene IDs
OrgDb required Organism database
keyType ENTREZID Input ID type
ont BP BP, MF, CC, or ALL
pvalueCutoff 0.05 P-value threshold
qvalueCutoff 0.2 Q-value (FDR) threshold
pAdjustMethod BH BH, bonferroni, etc.
universe NULL Background genes
minGSSize 10 Min genes per term
maxGSSize 500 Max genes per term
readable FALSE Convert to symbols

Related Skills

  • kegg-pathways - KEGG pathway enrichment
  • gsea - Gene Set Enrichment Analysis for GO
  • enrichment-visualization - Visualize enrichment results
  • differential-expression - Generate input gene lists

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results